Getting Started with R
Aldrin Carbonell
January 11, 2019
This is AldrinEC’s first assignment for Geog458. Our course page can be accessed here.
This is my favorite video game right now.
The formula to calculate the area of a circle is: \(\pi r^2\)
| Course Name | Quarter Completed |
|---|---|
| CSE 142 | Wi16 |
| MATH 124 | Sp16 |
| GEOG 360 | Au18 |
| GEOG 315 | Au18 |
Source: Aldrin Carbonell
Rudimentary R Practice
100/10+2## [1] 12100/(10+2)## [1] 8.33333380/(15+5)*2-30/6## [1] 345+5*2-10## [1] 45200-50*2+(5*3)-20## [1] 95x=8*3
x+10## [1] 34y=6
z=11
x*y## [1] 144(z+10)/3## [1] 7(y+z)*38## [1] 646nums = seq(1, 30)
strings = c("astronaut","ballerina","camera")
length(nums)## [1] 30length(strings)## [1] 3sum(nums)## [1] 465num2 = seq(1, 5)
num3 = seq(6, 10)
num2 + num3## [1] 7 9 11 13 15prod = num2 * num3
twoseqs = c(num2, num3)
rows = rbind(num2, num3, prod)
rows## [,1] [,2] [,3] [,4] [,5]
## num2 1 2 3 4 5
## num3 6 7 8 9 10
## prod 6 14 24 36 50numsdata = as.data.frame(rows)
numsdata## V1 V2 V3 V4 V5
## num2 1 2 3 4 5
## num3 6 7 8 9 10
## prod 6 14 24 36 50Loading the Data
this is how to load data into R and how to convert it.
library(tidyverse)## -- Attaching packages ---------------------------------------------------------------------------------------------------- tidyverse 1.2.1 --## v ggplot2 3.1.0 v purrr 0.2.5
## v tibble 1.4.2 v dplyr 0.7.8
## v tidyr 0.8.2 v stringr 1.3.1
## v readr 1.3.1 v forcats 0.3.0## -- Conflicts ------------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()object1=read.csv("China_EO_49to17.csv")
chinadata=as_tibble(object1)
rearrange = arrange(chinadata, desc(Year))
rearrange## # A tibble: 69 x 63
## Year Beijing_Enterpr~ Beijing_Output Tianjin_Enterpr~ Tianjin_Output
## <int> <int> <dbl> <int> <dbl>
## 1 2017 3231 NA 4286 NA
## 2 2016 3340 18087. 5203 27402.
## 3 2015 3548 17450. 5525 28017.
## 4 2014 3686 18453. 5501 28079.
## 5 2013 3641 17371. 5511 26400.
## 6 2012 3692 15596. 5342 23428.
## 7 2011 3746 14514. 5013 20863.
## 8 2010 6884 13700. 7947 16752.
## 9 2009 6890 11039. 8326 13084.
## 10 2008 7205 10413. 7950 12503.
## # ... with 59 more rows, and 58 more variables: Hebei_Enterprise <int>,
## # Hebei_Output <dbl>, Shanxi_Enterprise <int>, Shanxi_Output <dbl>,
## # InnerMongolia_Enterprise <int>, InnerMongolia_Output <dbl>,
## # Liaoning_Enterprise <int>, Liaoning_Output <dbl>,
## # Jilin_Enterprise <int>, Jilin_Output <dbl>,
## # Heilongjiang_Enterprise <int>, Heilongjiang_Output <dbl>,
## # Shanghai_Enterprise <int>, Shanghai_Output <dbl>,
## # Jiangsu_Enterprise <int>, Jiangsu_Output <dbl>,
## # Zhejiang_Enterprise <int>, Zhejiang_Output <dbl>,
## # Anhui_Enterprise <int>, Anhui_Output <dbl>, Fujian_Enterprise <int>,
## # Fujian_Output <dbl>, Jiangxi_Enterprise <int>, Jiangxi_Output <dbl>,
## # Shandong_Enterprise <int>, Shandong_Output <dbl>,
## # Henan_Enterprise <int>, Henan_Output <dbl>, Hubei_Enterprise <int>,
## # Hubei_Output <dbl>, Hunan_Enterprises <int>, Hunan_Output <dbl>,
## # Guangdong_Enterprise <int>, Guangdong_Output <dbl>,
## # Guangxi_Enterprise <int>, Guangxi_Output <dbl>,
## # Hainan_Enterprise <int>, Hainan_Output <dbl>,
## # Chongqing_Enterprise <int>, Chongqing_Output <dbl>,
## # Sichuan_Enterprise <int>, Sichuan_Output <dbl>,
## # Guizhou_Enterprise <int>, Guizhou_Output <dbl>,
## # Yunnan_Enterprise <int>, Yunnan_Output <dbl>, Tibet_Enterprise <int>,
## # Tibet_Output <dbl>, Shaanxi_Enterprise <int>, Shaanxi_Output <dbl>,
## # Gansu_Enterprise <int>, Gansu_Output <dbl>, Qinghai_Enterprise <int>,
## # Qinghai_Output <dbl>, Ningxia_Enterprise <int>, Ningxia_Output <dbl>,
## # Xinjiang_Enterprise <int>, Xinjiang_Output <dbl>year2k = filter(rearrange, Year >= 2000)
shang.bei = select(year2k, Year, Shanghai_Enterprise, Shanghai_Output, Beijing_Enterprise, Beijing_Output)
shang.bei## # A tibble: 18 x 5
## Year Shanghai_Enterpri~ Shanghai_Output Beijing_Enterpr~ Beijing_Output
## <int> <int> <dbl> <int> <dbl>
## 1 2017 8122 36094. 3231 NA
## 2 2016 8351 31136. 3340 18087.
## 3 2015 8994 31050. 3548 17450.
## 4 2014 9469 32665. 3686 18453.
## 5 2013 9796 32089. 3641 17371.
## 6 2012 9772 31548. 3692 15596.
## 7 2011 9962 32445. 3746 14514.
## 8 2010 16684 30114. 6884 13700.
## 9 2009 17906 24091. 6890 11039.
## 10 2008 18792 25121. 7205 10413.
## 11 2007 15099 22260. 6397 9648.
## 12 2006 14404 18573. 6400 8210
## 13 2005 14809 15768. 6300 6946.
## 14 2004 15766 12885. 6871 4881.
## 15 2003 11098 10343. 4019 3810.
## 16 2002 10057 7741. 4551 3173.
## 17 2001 9762 7004. 4356 2909.
## 18 2000 8574 6205. 4572 2565.finaltib = mutate(shang.bei, Output_Ratio = Beijing_Output / Shanghai_Output)
finaltib## # A tibble: 18 x 6
## Year Shanghai_Enterp~ Shanghai_Output Beijing_Enterpr~ Beijing_Output
## <int> <int> <dbl> <int> <dbl>
## 1 2017 8122 36094. 3231 NA
## 2 2016 8351 31136. 3340 18087.
## 3 2015 8994 31050. 3548 17450.
## 4 2014 9469 32665. 3686 18453.
## 5 2013 9796 32089. 3641 17371.
## 6 2012 9772 31548. 3692 15596.
## 7 2011 9962 32445. 3746 14514.
## 8 2010 16684 30114. 6884 13700.
## 9 2009 17906 24091. 6890 11039.
## 10 2008 18792 25121. 7205 10413.
## 11 2007 15099 22260. 6397 9648.
## 12 2006 14404 18573. 6400 8210
## 13 2005 14809 15768. 6300 6946.
## 14 2004 15766 12885. 6871 4881.
## 15 2003 11098 10343. 4019 3810.
## 16 2002 10057 7741. 4551 3173.
## 17 2001 9762 7004. 4356 2909.
## 18 2000 8574 6205. 4572 2565.
## # ... with 1 more variable: Output_Ratio <dbl>